Electric vehicle energy predictive optimal control by V2I communication. (February 2019)

• Record Type: Journal Article
• Authors: Bo, Zhu; Di, Wu; MinYi, Zheng; Nong, Zhang; Lin, He
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 2(2019)
• Publication Date: 2019-02
• Subjects: Electric vehicle -- predictive optimal control -- V2I -- intelligent transportation systems -- traffic information systems -- vehicle economics; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814018821523
• Languages: English
• ISSNs: 1687-8132

Abstract

To achieve a better energy-efficient and comfortable vehicle driving, electric vehicle energy predictive optimal control was discussed in this article. It used Vehicle to Infrastructure (V2I) communication of traffic light distance and time gap to make velocity decision in order to improve whole driving performance. Based on the data of standard New European Driving Cycle, this article put forward a real-time cycle including traffic light position and time information at first. Then, a preview and optimal decision controller was built. Four evaluating indexes were set for optimal decision making to decide the optimal expected speed, according to the pass time that the forward traffic lights allowed and the distance or the forward vehicle speed and distance. After that AMESim and Simulink co-simulation platform was used to build the vehicle dynamics model and to exploit the control algorithm. The simulation results indicated that vehicle with V2I can effectively improve the operating condition of the whole vehicle and reduce the frequency of vehicle starting and stopping, which makes the vehicle driving smoother and steady. Moreover, it can reduce vehicle energy consumption and improve endurance mileage. Meanwhile, the vehicle acceleration and jerk have decreased dramatically with better comfortable performance.